Temporal scope adds a time dimension to facts in Knowledge Bases (KBs). These time scopes specify the time periods when a given fact was valid in real life. Without temporal scope, many facts are underspecified, reducing the usefulness of the data for upper level applications such as Question Answering. Existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we present a new method that leverages temporal profiles augmented with context—Contextual Temporal Profiles (CTPs) of entities. Through change patterns in an entity’s CTP, we model the entity’s state change brought about by real world events that happen to the entity (eg, hired, fired, divorced, etc.). This leads to a new formulation of the temporal scoping problem as a state change detection problem. Our experiments show that this formulation of the problem, and the resulting solution are highly effective for inferring temporal scope of facts.